Bridging genomics' greatest challenge: The diversity gap

Manuel Corpas; Mkpouto Pius; Marie Poburennaya; Heinner Guio; Miriam Dwek; Shivashankar Nagaraj; Catalina Lopez-Correa; Alice Popejoy; Segun Fatumo

doi:10.1016/j.xgen.2024.100724

Bridging genomics' greatest challenge: The diversity gap

Cell Genom. 2024 Dec 12:100724. doi: 10.1016/j.xgen.2024.100724. Online ahead of print.

Authors

Manuel Corpas¹, Mkpouto Pius², Marie Poburennaya³, Heinner Guio⁴, Miriam Dwek², Shivashankar Nagaraj⁵, Catalina Lopez-Correa⁶, Alice Popejoy⁷, Segun Fatumo⁸

Affiliations

¹ Life Sciences, University of Westminster, 115 New Cavendish Street, W1W 6UW London, UK; The Alan Turing Institute, London, UK; Cambridge Precision Medicine Ltd., ideaSpace, University of Cambridge Biomedical Innovation Hub, Cambridge, UK. Electronic address: [email protected].
² Life Sciences, University of Westminster, 115 New Cavendish Street, W1W 6UW London, UK.
³ Queen Mary University of London, London, UK.
⁴ INBIOMEDIC Research and Technological Center, Lima, Peru.
⁵ Centre for Genomics and Personalised Health, Queensland University of Technology, Brisbane, QLD, Australia.
⁶ Genome Canada, Ottawa, ON, Canada.
⁷ Department of Public Health Sciences (Epidemiology), School of Medicine, University of California, Davis, Davis, CA, USA; UC Davis Comprehensive Cancer Center (UCDCCC), UC Davis Health, University of California, Davis, Sacramento, CA, USA.
⁸ African Computational Genomics (TACG) Research Group, The MRC Uganda Medical Informatics Centre (UMIC), MRC/UVRI and LSHTM, Entebbe, Uganda; Precision Health University Research Institute, Queen Mary University of London, London, UK.

PMID: 39694036
DOI: 10.1016/j.xgen.2024.100724

Abstract

Achieving diverse representation in biomedical data is critical for healthcare equity. Failure to do so perpetuates health disparities and exacerbates biases that may harm patients with underrepresented ancestral backgrounds. We present a quantitative assessment of representation in datasets used across human genomics, including genome-wide association studies (GWASs), pharmacogenomics, clinical trials, and direct-to-consumer (DTC) genetic testing. We suggest that relative proportions of ancestries represented in datasets, compared to the global census population, provide insufficient representation of global ancestral genetic diversity. Some populations have greater proportional representation in data relative to their population size and the genomic diversity present in their ancestral haplotypes. As insights from genomics become increasingly integrated into evidence-based medicine, strategic inclusion and effective mechanisms to ensure representation of global genomic diversity in datasets are imperative.

Keywords: GWAS; ancestry; bias; direct-to-consumer; diversity; equity; genomics; global datasets; healthcare; inclusion; pharmacogenetics; representation.

Publication types

Review